深度学习论文笔记：Rich feature hierarchies for accurate object detection and semantic segmentation
Abstract mAP: mean average precision，平均准确度 咱们的方法结合两个关键的看法：web 第一：采用高容量的卷积神经网络来从上到下的进行region proposal，从而实现定位和分割物体。 当标记的训练数据稀缺时，能够先对辅助数据集（任务）进行受监督的预训练， 随后是基于域进行特定调整，产生显着的性能提高。 Introduction 关于各类视觉识别任务的上一
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相关文章
- 1. Rich feature hierarchies for accurate object detection and semantic segmentation
- 2. 论文笔记：Rich feature hierarchies for accurate object detection and semantic segmentation
- 3. 【论文阅读笔记】Rich feature hierarchies for accurate object detection and semantic segmentation
- 4. 论文：Rich feature hierarchies for accurate object detection and semantic segmentation 个人笔记
- 5. 论文笔记：Rich feature hierarchies for accurate object detection and semantic segmentation(R-CNN)
- 6. 论文学习《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 7. 【论文学习】Rich feature hierarchies for accurate object detection and semantic segmentation
- 8. RCNN学习笔记(1):Rich feature hierarchies for accurate object detection and semantic segmentation
- 9. 【论文阅读】Rich feature hierarchies for accurate object detection and semantic segmentation
- 10. R-CNN论文翻译Rich feature hierarchies for accurate object detection and semantic segmentation